MLOps Engineer

At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds. As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered and recognised.

What You Can Expect

Job Summary

The MLOps Engineer is responsible for designing, building, and operating scalable, reliable, and secure machine learning platforms and pipelines. This role bridges data science, software engineering, and cloud infrastructure, enabling models to move from experimentation to production with high availability, governance, and performance.The role focuses on ML platform engineering, automation, reliability, and lifecycle management across training, deployment, monitoring, and retraining of machine learning models.

Work Location: Bangalore

Work Mode: Hybrid (3 Days in office)

How You'll Create Impact

Key Responsibilities

ML Platform & Pipeline Engineering

Design, build, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoringProductionize machine learning models developed by Data ScientistsImplement standardized workflows for feature engineering, model versioning, and model promotion

Deployment, Monitoring & Reliability

Deploy models using containerized and cloud-native architecturesImplement monitoring for model performance, data drift, and system healthLead root-cause analysis for model or pipeline failures and implement long-term fixes

Automation & DevOps for ML

Build CI/CD pipelines for ML workflows (training, testing, deployment)Automate infrastructure provisioning and environment managementEnforce repeatability, reproducibility, and traceability of ML experiments

Governance, Security & Compliance

Implement ML governance controls including lineage, auditability, and access controlPartner with Security, GRC, and Data Governance teams to ensure complianceSupport responsible AI practices and enterprise standards

Collaboration & Enablement

Partner closely with Data Scientists, Data Engineers, and Platform EngineersProvide guidance and best practices for scalable model developmentContribute to documentation, standards, and internal enablement

What Makes You Stand Out

Technologies & Tools

Machine Learning & MLOps

Python (primary), with ML libraries (scikit-learn, TensorFlow, PyTorch – support level)MLflow, Kubeflow, or similar ML lifecycle toolsFeature stores (e.g., Feast, cloud-native feature stores)Model registries and experiment tracking

Data & Pipeline Engineering

Workflow orchestration tools (e.g., Airflow, Dagster, Prefect)Data processing frameworks (Spark, distributed data processing concepts)SQL and data warehousing fundamentals

Cloud & Infrastructure

Cloud platforms: AWS, Azure, or GCP (at least one)Containerization: DockerOrchestration: KubernetesInfrastructure as Code: Terraform, ARM/Bicep, or CloudFormation

DevOps & CI/CD

CI/CD tools (GitHub Actions, GitLab CI, Azure DevOps, Jenkins)Version control: GitMonitoring and logging (Prometheus, Grafana, Cloud-native monitoring tools)

Security & Governance

Identity and access management (RBAC, secrets management)Data privacy and model governance conceptsExposure to regulated or SOX-controlled environments (preferred)

Your Background

Required Qualifications

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)

Years Of Experience

5–8 years of experience in software engineering, data engineering, or platform engineering3+ years of hands-on experience in MLOps, ML platform engineering, or ML deploymentExperience supporting production-grade machine learning systems

Preferred Qualifications

7+ years total engineering experienceExperience supporting real-time or near-real-time ML inferenceExperience with model monitoring, drift detection, and retraining automationExperience working in enterprise or regulated environmentsCertifications in cloud platforms or data/ML engineering (preferred)

Core Competencies

Strong systems and platform engineering mindsetAdvanced troubleshooting and problem-solving skillsAbility to translate research models into reliable production systemsClear communication across Data Science, Engineering, and ITStrong ownership for reliability, scalability, and securityExperience operating in cloud-based, distributed environments

Physical Requirements

Travel Expectations

EOE/M/F/Vet/Disability